{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = (0, 1)\n",
    "y = (5, 9)\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.title(\"test\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = [a for a in range(1, 10)]\n",
    "y1 = [a * a for a in x]\n",
    "# y2 = [a + 5 for a in y1]\n",
    "plt.xlim(0, max(x) + 1)  # 稍微扩展范围以确保(0,0)在内\n",
    "plt.ylim(0, max(y) + 1)\n",
    "plt.plot(x, y1, marker='o')\n",
    "# plt.plot(x, y2, marker=\"o\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 假设这是你的数据点\n",
    "x = [0, 1, 2, 3, 4, 5]\n",
    "y = [20, 40, 60, 80, 2, 4]\n",
    "\n",
    "# 绘制数据点\n",
    "plt.plot(x, y, \"o-\")  # 'o-' 表示线段连接点，并在点上画圆圈\n",
    "\n",
    "# 设置 x 轴和 y 轴的范围，确保(0,0)在左下角\n",
    "plt.xlim(min(x) - 1, max(x) + 1)  # 稍微扩展范围以确保(0,0)在内\n",
    "plt.ylim(min(y) - 1, max(y) + 1)\n",
    "\n",
    "# 设置图表标题和轴标签\n",
    "plt.title(\"Plot with (0,0) Aligned\")\n",
    "plt.xlabel(\"X Axis\")\n",
    "plt.ylabel(\"Y Axis\")\n",
    "\n",
    "# 调整子图参数以填充整个图形区域\n",
    "plt.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0, wspace=0)\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 假设这是你的数据点，横坐标使用月份名称\n",
    "months = [\"一月\", \"二月\", \"三月\", \"四月\", \"五月\"]\n",
    "y = [[20, 40, 60, 80, 100], [10, 20, 80, 30, 50]]  # 这里使用示例数据\n",
    "\n",
    "labels = [\"公安分局\", \"法院\"]\n",
    "# 绘制数据点，使用'o-'表示线段连接点，并在点上画圆圈\n",
    "for i,j in y, labels:\n",
    "    print(i, j)\n",
    "    plt.plot(months, i, marker=\"o\", label=j)\n",
    "\n",
    "# 设置中文用楷体\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"KaiTi\"]\n",
    "\n",
    "# 设置图表标题和轴标签\n",
    "plt.title(\"Monthly Data Plot\")\n",
    "plt.xlabel(\"月份\")  # 这里使用英文的'Month'作为标签，你可以根据需要更改为中文\n",
    "plt.ylabel(\"产量\")\n",
    "\n",
    "# 设置x轴和y轴的范围\n",
    "plt.xlim(\"一月\", \"五月\")\n",
    "plt.ylim(0, 110)\n",
    "\n",
    "# 设置x轴的刻度标签为月份名称\n",
    "plt.xticks(months)\n",
    "\n",
    "# 添加图例\n",
    "plt.legend()\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 定义数据点\n",
    "x = [1, 2, 3, 4, 5]\n",
    "y1 = [1, 4, 9, 16, 25]\n",
    "y2 = [2, 8, 18, 32, 50]\n",
    "\n",
    "# 绘制第一条线\n",
    "plt.plot(x, y1, label=\"Line 1\", color=\"blue\")\n",
    "# 为第一条线添加文本标注\n",
    "# plt.text(\n",
    "#     x[2],\n",
    "#     y1[2],\n",
    "#     \"Line 1 Label\",\n",
    "#     fontsize=12,\n",
    "#     verticalalignment=\"bottom\",\n",
    "#     horizontalalignment=\"center\",\n",
    "# )\n",
    "\n",
    "# 绘制第二条线\n",
    "plt.plot(x, y2, label=\"Line 2\", color=\"red\", linestyle=\"--\")\n",
    "# 为第二条线添加文本标注\n",
    "# # plt.text(\n",
    "#     x[2],\n",
    "#     y2[2],\n",
    "#     \"Line 2 Label\",\n",
    "#     fontsize=12,\n",
    "#     verticalalignment=\"top\",\n",
    "#     horizontalalignment=\"center\",\n",
    "# )\n",
    "\n",
    "# 添加图例\n",
    "plt.legend()\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[([20, 40, 60, 80, 100], '公安分局'), ([10, 20, 80, 30, 50], '法院')]\n",
      "[20, 40, 60, 80, 100]\n",
      "[10, 20, 80, 30, 50]\n",
      "公安分局\n",
      "法院\n"
     ]
    }
   ],
   "source": [
    "y = [[20, 40, 60, 80, 100], [10, 20, 80, 30, 50]]  # 这里使用示例数据\n",
    "\n",
    "labels = [\"公安分局\", \"法院\"]\n",
    "\n",
    "print(list(zip(y, labels)))\n",
    "# 绘制数据点，使用'o-'表示线段连接点，并在点上画圆圈\n",
    "for i, j in y, labels:\n",
    "    print(i)\n",
    "    print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[10000, 9801, 9604, 9409, 9216, 9025, 8836, 8649, 8464, 8281, 8100, 7921, 7744, 7569, 7396, 7225, 7056, 6889, 6724, 6561, 6400, 6241, 6084, 5929, 5776, 5625, 5476, 5329, 5184, 5041, 4900, 4761, 4624, 4489, 4356, 4225, 4096, 3969, 3844, 3721, 3600, 3481, 3364, 3249, 3136, 3025, 2916, 2809, 2704, 2601, 2500, 2401, 2304, 2209, 2116, 2025, 1936, 1849, 1764, 1681, 1600, 1521, 1444, 1369, 1296, 1225, 1156, 1089, 1024, 961, 900, 841, 784, 729, 676, 625, 576, 529, 484, 441, 400, 361, 324, 289, 256, 225, 196, 169, 144, 121, 100, 81, 64, 49, 36, 25, 16, 9, 4, 1, 0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 361, 400, 441, 484, 529, 576, 625, 676, 729, 784, 841, 900, 961, 1024, 1089, 1156, 1225, 1296, 1369, 1444, 1521, 1600, 1681, 1764, 1849, 1936, 2025, 2116, 2209, 2304, 2401, 2500, 2601, 2704, 2809, 2916, 3025, 3136, 3249, 3364, 3481, 3600, 3721, 3844, 3969, 4096, 4225, 4356, 4489, 4624, 4761, 4900, 5041, 5184, 5329, 5476, 5625, 5776, 5929, 6084, 6241, 6400, 6561, 6724, 6889, 7056, 7225, 7396, 7569, 7744, 7921, 8100, 8281, 8464, 8649, 8836, 9025, 9216, 9409, 9604, 9801]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1f19196010>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/congyu/anaconda3/lib/python3.11/site-packages/IPython/core/events.py:82: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/home/congyu/anaconda3/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "x = [x for x in range(-100, 100)]\n",
    "y = [a * a for a in x]\n",
    "\n",
    "print(x)\n",
    "print(y)\n",
    "\n",
    "plt.plot(x, y, marker=\"o\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
